175 results about "Expectation–maximization algorithm" patented technology

The invention discloses a line intelligent planning and fault early warning method suitable for electric power engineering design, and relates to the technical field of electric power engineering. The method comprises the following steps: quantifying geographic information, geological conditions and construction cost into a continuous decision curved surface through spatial semantic segmentation and a cost curved surface model, and identifying a large-area low-cost region as a primary feasible region in combination with a dynamic clustering algorithm; a multi-dimensional decision index is constructed based on spatial features, geological stability and facility association data, fault regions such as terrain abrupt change or facility conflict are identified through real-time association coefficients, and an exception handling mechanism is triggered; and selecting a seed region with an optimal construction condition as an anchor point, constructing a joint probability model of spatial features and cost, and generating an optimal path considering feasibility probability and cost effectiveness in combination with an expectation maximization algorithm and a graph optimization technology. According to the invention, scientificity and economical efficiency of cable planning under complex geological conditions are improved, and key technical support is provided for intelligentization of electric power engineering.

The invention discloses a mechanical structure reliability evaluation method based on a hybrid R-Vine Copula model, and the method comprises the steps: firstly, selecting an optimal R-Vine tree type according to sample data by employing an AIC criterion and the basic definition of an R-Vine tree type, employing a hybrid Pair-Copula function for the first layer of R-Vine tree type to improve the sample adaptability, employing Clayton, Gumbel and Frank Copula functions to construct a hybrid Copula function, and employing a Pair-Copula function to construct a Copula model; calculating a weight coefficient and a dependent parameter of the mixed Pair-Copula function by using an expectation maximization algorithm; a subsequent tree type is constructed by adopting a single Pair-Copula function, so that the optimal balance between the calculation efficiency and the calculation precision is achieved, and the optimal construction based on the mixed R-Vine Copula model is completed; and finally, solving the reliability index and the failure probability of the structure based on a mixed R-Vine Copula model in combination with an improved first-order second-order moment method.

The invention discloses a lithium battery residual life prediction method based on a multi-stage Wiener process, and relates to the technical field of lithium batteries, and the method comprises the steps: building a lithium battery degradation model based on the Wiener process; self-adaptively estimating a degradation state and model parameters based on an expectation maximization (EM) algorithm and a Kalman smoothing method; and in order to predict the residual life at the current moment, considering the condition that the degradation state at the change point is unknown, solving a probability density function of the residual life, and obtaining an estimated value of the residual life. According to the lithium battery residual life prediction method based on the multi-stage Wiener process, the degradation model based on the multi-stage Wiener process with the measurement error is established, the probability density function of the residual life is solved and simplified, and the accuracy of lithium battery residual life prediction is improved.

The invention discloses a bad weather degraded image restoration method and device based on dynamic degradation generation, and relates to the field of image processing, and the method comprises the steps: employing an E step of an expectation maximization algorithm to optimize a hidden variable in a training process of a noise predictor and a dynamic degradation generator, and obtaining an optimized hidden variable; in M steps of an expectation maximization algorithm, inputting the optimized hidden variables and state variables into a dynamic degradation generator, generating a degradation layer to perform semi-supervised training on a noise predictor and the dynamic degradation generator, and obtaining a trained noise predictor; in an image restoration stage, an image degraded in severe weather and random Gaussian noise are input into a trained noise predictor, an iterative denoising step obeying a Markov chain is executed, and a clean restored image is obtained. The problems that an existing severe weather image restoration model is limited in generalization ability and unstable in restoration effect are solved.

The invention discloses a bearing residual life prediction method and device and a medium, and relates to the field of bearing life prediction, and the method comprises the steps: carrying out the feature extraction of a vibration signal time sequence of a target bearing, and obtaining a time domain feature, performing time domain feature screening, feature weight assignment and weighted calculation based on robustness, monotonicity and predictability to obtain a standby feature vector; performing multiple regression operation on the to-be-used time domain feature set and the to-be-used feature vector set by adopting a partial least square method to realize feature fusion to obtain a comprehensive degradation feature quantity; and on the basis of the comprehensive degradation characteristic quantity corresponding to each vibration signal time sequence, constructing a prediction model by adopting a preset index model, performing parameter updating and estimation on the prediction model by adopting a Bayesian updating and expectation maximization algorithm, and performing prediction error correction by adopting a strong tracking filtering algorithm so as to obtain a bearing residual life prediction result. The method can improve the prediction precision of the residual life of the bearing.

The invention discloses a model training method and device, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a weight positive sample training set and a weight negative sample training set, the weight positive sample training set and the weight negative sample training set are obtained by labeling and screening a preset unlabeled sample set; based on a preset unbiased positive sample and unmarked sample learning algorithm, according to the weight positive sample training set and the weight negative sample training set, training to obtain a dichotomy model; iterating the dichotomy model through a loss function and a regular function corresponding to the dichotomy model based on a preset expectation maximization algorithm until the dichotomy model converges, and obtaining a converged dichotomy model; and taking the converged dichotomy model as a prediction model. The problems that an existing method is poor in universality and generalization and needs to label samples manually are solved, and the universality and generalization of model training are improved.

The invention relates to the technical field of target tracking, and discloses a maneuvering target tracking system and method based on hybrid coding and UKF decoding. The maneuvering target tracking method comprises the steps of obtaining radar measurement data of a target position at each moment based on a target trajectory; the method comprises the following steps: encoding radar measurement data by adopting an attention-GRU network to obtain a target state encoding value; based on the target state coding value, obtaining a corrected target state noise covariance matrix, a corrected target state measurement noise covariance matrix and a corrected state initial covariance matrix by adopting an expectation maximization algorithm; and performing UKF decoding calculation on radar measurement data by adopting the corrected target state noise covariance matrix, the corrected target state measurement noise covariance matrix and the corrected state initial covariance matrix to obtain a target position. Model parameters such as noise variance information do not need to be preset, the tracking precision of the maneuvering target can be effectively improved, and noise interference is weakened.

The invention discloses a multi-dimensional distribution offset adaptive threshold detection method and system, and the method comprises the steps: obtaining real-time data, extracting a multi-dimensional statistical feature, and obtaining a feature vector; based on historical normal data, using an expectation maximization algorithm to train a Gaussian mixture model, and determining parameters to obtain a normal distribution model; inputting the feature vector into the model, and calculating a probability value of the feature vector belonging to normal distribution as a first offset judgment index; based on the real-time data distribution of a plurality of detection objects in the same group, the distribution difference of any two objects is calculated by using a Wasserstein distance, and the similarity between the objects is obtained; and constructing a similarity network and calculating connectivity as a second offset judgment index. Setting a fixed-length sliding window, dynamically updating two indexes in the window, and obtaining a first self-adaptive threshold value and a second self-adaptive threshold value; and when any index is lower than a corresponding threshold value, determining distribution offset and giving an alarm, and updating model parameters in real time by using an incremental expectation maximization algorithm. According to the invention, accurate detection and intelligent analysis of data distribution offset are realized.

The invention provides a communication auxiliary sensing system and method based on an intelligent super surface (RIS). The communication auxiliary sensing system comprises a multi-antenna base station, a multi-user terminal and an active RIS. According to the system, an uplink pilot frequency and a data signal are jointly utilized, an expectation maximization (EM) algorithm is adopted to iteratively recover equivalent channel state information (CSI), and a Cramer-Rao bound (CRB) of sensing performance is deduced. Combined optimization of user precoding and RIS reflection coefficients is innovatively proposed under the uncertainty of CSI, so as to minimize CRB and guarantee user communication rate at the same time. An original problem is converted into a processable form by establishing a traversal MIMO spectrum efficiency compact lower bound, and efficient optimization is realized by combining a WMMSE method with a block coordinate descent (BCD) frame. According to the scheme, the sensing precision and the spectrum efficiency are remarkably improved, the method is suitable for an active RIS-assisted MU-MIMO network, and the technology can also be expanded to a traditional RIS-free system.

The invention provides a vulnerability repair method based on a dynamic denoising and hybrid enhancement technology, which comprises the following steps: S1, performing dynamic denoising and hybrid enhancement on an original vulnerability repair model to obtain an optimized vulnerability repair model; s11, deriving approximate distribution of clean data and noise data from the training loss of the original vulnerability repair model by using an expectation maximization algorithm; s12, calculating the weight of the clean data and the weight of the noise data by using a confidence coefficient function of distribution perception, and re-weighting the approximate distribution by using the weight of the clean data and the weight of the noise data; s13, generating a mixed sample by using the interpolation coefficient and reconstructing a loss function to obtain a total loss value; s14, repeatedly training the vulnerability repair model by using the value of the total loss until the value of the current total loss is not reduced in continuous n rounds or reaches the total number of rounds of training, and outputting the optimized vulnerability repair model; and S2, performing vulnerability repair by using the optimized vulnerability repair model. The software development efficiency and quality are improved, and software vulnerabilities are reduced.

The invention discloses a deception jamming signal identification and interference source positioning method and system, and belongs to the technical field of signal identification and interference source positioning, and the method comprises the steps: receiving intermediate frequency receiving signals of a global satellite navigation system, including a real satellite signal, a forwarding deception jamming signal, a multipath signal and a noise signal; performing multi-component parameter estimation based on a space alternating generalized expectation maximization algorithm to obtain a parameter set comprising time delay, Doppler frequency shift and complex amplitude; according to consistency detection of receiver clock differences and Doppler frequency shift information of a plurality of adjacent base stations, identifying deception jamming components and real signal components from the parameter set; and determining the position of an interference source by adopting a delay inequality positioning algorithm based on the arrival delay inequality of the deception interference component among the plurality of adjacent base stations. According to the method, the GNSS signal characteristics are utilized, space-power time-frequency joint detection is realized with low complexity under the condition of not needing extra hardware cost, and the performance of deception jamming detection in practical application is improved.

The invention discloses a fiber laminate damage acoustic emission signal processing method, equipment and a medium, and relates to the technical field of signal processing.The method comprises the steps that a fiber laminate is fixed to a test device, signals are collected through an acoustic emission sensor, filtering and preprocessing are carried out, features are extracted through an improved ReliefF algorithm, and a fiber laminate damage acoustic emission signal is obtained; randomly selecting samples, comparing the samples with nearest neighbors, updating feature weights, and screening most relevant features. An expectation maximization algorithm and a Gaussian mixture model are combined to establish a damage identification model, different types of damage are automatically classified, and a Gaussian distribution model is obtained through iterative optimization. And by utilizing a clustering analysis and feature selection algorithm, the damage of the laminated plate with different layering angles is identified, and different damage types are accurately distinguished. According to the method, high-precision damage classification is realized through feature extraction in combination with an expectation maximization algorithm and a Gaussian mixture model, laminated plate damage analysis at different layering angles is dynamically adapted, and feature extraction efficiency, classification accuracy and model adaptability are remarkably improved.

The invention belongs to the technical field of wireless communication, and particularly relates to a positioning method based on low-orbit satellite opportunity signal Doppler measurement. The method solves the Doppler measurement positioning problem based on low orbit (LEO) satellite opportunity signals under the condition that GNSS signals are limited or unavailable. A positioning process is modeled as a non-linear state space framework, a joint estimation scheme combining a cubic Kalman smoother and an expectation maximization algorithm is provided, efficient estimation of a terminal position and Doppler frequency shift is realized, and noise covariance is adaptively optimized at the same time. The method provided by the invention theoretically ensures the optimal performance in statistical significance. A simulation result shows that compared with a traditional nonlinear least square method, the scheme provided by the invention can remarkably improve the positioning precision, and the calculation overhead is kept reasonable and balanced.

The invention relates to the technical field of voice recognition, and discloses a voiceprint model training method, a user identity detection method and device, and an intelligent household electrical appliance, and the voiceprint model training method comprises the steps: carrying out the preprocessing of a voice signal, and extracting a mixed feature vector containing a static frequency spectrum feature and a time difference feature; training a Gaussian mixture model to be converged by adopting an expectation maximization algorithm based on the mixed feature vector; and outputting the special voiceprint model of the speaker. The voiceprint model obtained by training through the training method is used for user identity detection, and high robustness, high real-time performance and high accuracy of voiceprint recognition in a complex noise environment are achieved.

The invention discloses a natural electric field-electric field joint detection and inversion method based on GMM prior, and relates to the technical field of tunnel engineering geological detection, and the method comprises the steps: obtaining the resistivity and natural potential observed in a to-be-inverted region; an equivalent body source of a natural potential is parameterized into a body source vector on a grid unit, weighted L1 sparse regular constraint is introduced into the body source vector, a GMM prior model is used as underground electrical structure prior shared across physics fields, and a multi-task joint inversion objective function of a shared underground electrical structure label is established; and performing inversion iteration by adopting maximum posteriori, updating parameters of the GMM prior model and the underground electrical structure label by adopting an expectation maximization algorithm until an end condition is met, and obtaining a joint inversion imaging result of the tunnel water-rich anomalous body. The advantages of a multi-source electrical method are fused, underground electrical structure priori is introduced, Bayesian uncertainty analysis is combined, and high-resolution and high-reliability recognition of the water-rich anomalous body in front of the tunnel is achieved.

The invention discloses an electronic device residual life online prediction method and system under an operation condition, a storage medium and electronic equipment, and the method comprises the steps: building a Gamma state space model, and carrying out the processing through an expectation maximization algorithm, and obtaining an initial parameter estimation value of the Gamma state space model; and based on the initial parameter estimation value and pre-acquired real-time monitoring data of the field operation device, dynamically updating the real health state of the electronic device through a kernel smoothing particle filter algorithm, and estimating new parameter estimation of the Gamma state space model by using an expectation maximization algorithm to obtain a final Gamma state space model. According to the method, the degradation information is extracted and the initial degradation model is established based on the accelerated failure experiment data, and then the degradation information and the model parameters are dynamically updated by adopting the kernel smoothing particle filter algorithm in combination with the field operation data, so that the real-time life prediction under the field operation condition is realized.

The invention provides an earthquake landslide disaster chain risk assessment method, medium and equipment, and relates to the technical field of geological disaster chain risk assessment, and the method comprises the steps: obtaining earthquake-induced disaster chain data, and constructing an earthquake landslide disaster chain knowledge graph; according to the knowledge graph, acquiring disaster influence factors, determining association among the disaster influence factors, and determining key disaster influence factors with relatively high association degree; key disaster influence factors are used as Bayesian network nodes, node variable data are discretized, a maximum-minimum hill climbing algorithm is used for structural learning, and a directed acyclic graph structure of the Bayesian network is determined; performing Bayesian network parameter learning by using an expectation maximization algorithm to obtain a prior probability of Bayesian network nodes; if the prediction result of the model does not reach the expectation, the association between the nodes is adjusted, and if the prediction result reaches the expectation, the model is used for earthquake landslide disaster chain risk assessment. According to the scheme, the reliability of the prediction result can be improved.

The invention discloses a Bayesian network-based rainstorm-mountain torrent disaster chain trigger threshold identification method, which comprises the following steps: according to a preset fuzzy character string matching algorithm and a preset text similarity algorithm, carrying out cleaning processing on extracted historical disaster situation texts in sequence to obtain processed disaster situation texts; performing feature extraction on the processed disaster text to obtain key disaster features; constructing a target Bayesian network topology of the rainstorm-mountain torrent disaster chain; discretizing each node variable in the target Bayesian network topology to obtain a state value of the node variable; the prior probability of the node variables is calculated, and the conditional probability of the causal relationship between the nodes is calculated according to the prior probability and a preset application expectation maximization algorithm; calculating a target posterior probability that the first disaster triggers the derived disaster; and according to a preset posterior probability critical value and the target posterior probability, inverting the intensity of the first disaster, and determining an intensity threshold value of the derived disaster triggered by the first disaster.

The invention discloses a personalized picture book intelligent recommendation system based on combination of innovative sentence vectors and a hidden semi-Markov model, which belongs to the technical field of education and comprises a data acquisition module, a sentence vector generation module, an interest tracking module, a recommendation generation module, the innovative sentence vectors, the hidden semi-Markov model and an expectation maximization (EM) algorithm. According to the method, an efficient and accurate personalized picture book recommendation system is successfully constructed through deep fusion of the innovative sentence vectors and the HSMM, the problem of interest drifting of students in the reading process is effectively solved, the innovative sentence vectors can comprehensively capture deep semantics and emotional tendencies of reading texts of the students, and the interestingness of the students is improved. A rich and multi-dimensional data basis is provided for interest analysis, the HSMM model allows the students to stay for different time durations in different potential interest states, the heterogeneity of the interest duration of the students is accurately described, and dynamic tracking of interest changes of the students is achieved.

The invention discloses a method and a system for measuring a time average velocity field of a combustion chamber of a scramjet engine, and belongs to the technical field of average velocity field measurement. The method comprises the following steps: establishing a measurement system, matching the number of exposure frames of a high-speed camera with the number of cavities of a laser, collecting a time sequence image for pre-processing, obtaining the pre-processed time sequence image, dividing the time sequence image into grid slices for cross-correlation calculation, and obtaining a time sequence speed according to a space and pixel conversion coefficient; constructing a velocity model according to a Gaussian mixture model algorithm, and obtaining a probability matrix of a data point belonging to a correct velocity vector according to an expectation maximization algorithm; and calculating to obtain a time average velocity field according to the correct velocity vector and the error velocity vector judgment condition. The method effectively improves the efficiency and accuracy of measuring the distribution of the time average velocity field, and can be used for measuring the time average velocity field.

The invention relates to the technical field of satellite navigation positioning, in particular to a Beidou robust Kalman filtering positioning method based on a non-Gaussian noise model. According to the method, non-Gaussian noise generated by shielding and a multipath effect in Beidou positioning is accurately represented by constructing a Gaussian mixture model and a student t distribution model, model parameters are updated online by an expectation maximization algorithm, and the problem that a traditional Gaussian hypothesis model cannot adapt to noise statistical characteristics is solved; the non-Gaussian noise model is fused with robust filtering, a self-adaptive robust weight matrix is constructed through a Huber cost function, an IGGIII scheme or noise probability density, an observation covariance matrix is dynamically adjusted, the influence of abnormal observation values is suppressed, the defects that classical robust filtering lacks noise modeling and weight adjustment fails under continuous strong interference are overcome, and the robustness of the robust filtering is improved. And meanwhile, the problem of complex particle filtering calculation is avoided.

The invention discloses an Internet of Things anomaly detection method and system based on quaternion state space diffusion enhancement, and belongs to the technical field of network security and artificial intelligence. The method comprises the following steps: mapping a flow time sequence feature into a quaternion tensor to maintain an internal coupling relationship of a multi-dimensional feature; a double-flow encoder is designed, a quaternion selective state space model is adopted to extract continuous fluid features, and a dynamic hypergraph neural network is adopted to model discrete protocol features; carrying out self-supervised pre-training on a resistance pseudo-anomaly sample by utilizing potential diffusion model generation, and optimizing characterization by combining quaternion cepstrum distance loss; the injected learnable prompt vector is optimized in the small sample fine tuning stage, and a category prototype is corrected by using a semi-supervised expectation maximization algorithm; and calculating a sample anomaly score based on an energy model to realize known attack classification and unknown anomaly judgment. According to the method, the generalization ability of the model under the small sample condition and the unknown threat detection ability are remarkably improved.

The present invention relates to the technical field of data communication, and relates to a precise time synchronization clock parameter estimation method based on optimal invariant estimation. In the present invention, in a communication scene where unknown random time delay distribution information is present, on the basis of a precise time protocol, timestamp interaction is achieved by means of bidirectional data packet transfer between a master node and a slave node, and a clock relation model between the master node and the slave node is established; considering that a plurality of intermediate devices exist in the data packet interaction process, resulting that random time delay distribution information cannot be accurately obtained, a Gaussian mixture model is used to model a random time delay distribution, a competitive expectation maximization algorithm is used to learn mixture model parameters; and finally, on the basis of the parameters obtained by learning, an optimal invariant estimation method is used to estimate a clock frequency offset and a clock phase offset simultaneously, so as to realize time synchronization between the master node and the slave node. The present invention improves the accuracy of clock frequency offset estimation and clock phase offset estimation, is suitable for a communication scene where unknown random time delay distribution information is present, and enhances the robustness of the synchronization algorithm to a communication time delay.

The invention provides a cluster-based control system data statistics method and system. The method comprises the following steps: determining a multi-dimensional time sequence state data core object of a control system; starting from any unaccessed core object, forming an initial data cluster, fitting a Gaussian mixture model by using an expectation maximization algorithm, and extracting a parameter set of the Gaussian mixture model as statistical characteristics of the cluster; iteratively calculating the Bhattacharyya distance between the Gaussian mixture models of any two data clusters, and when the distance is smaller than a preset merging threshold value, re-fitting the Gaussian mixture model for the merged new clusters until no cluster can be merged; in any data cluster, cluster generation and combination are carried out again; data points which do not belong to any cluster are regarded as noise points, a noise point set of the region is regarded as a new data set according to the space-time density, and cluster generation, merging and splitting steps are executed on the new data set to generate a new cluster; and outputting the Gaussian mixture model parameter set of all the current data clusters as a statistical result of the current working condition of the control system.

The invention discloses a time sequence data truth value discovery method based on kernel density estimation, which comprises the following steps of: initializing necessary parameters such as bandwidth and credibility weight for each object and time point; a kernel density estimation stage: performing kernel density estimation on data of each data source at each time point by using a Gaussian kernel function, updating kernel density estimation by using an exponentially weighted recursive formula so as to reflect time dependence of time sequence data, calculating a joint probability of given known data for a missing value, and calculating a kernel density estimation result; kernel density estimation is used to assess the likelihood of missing data. Kernel estimation of multi-source data is carried out, and the credibility of different source data sets is considered; in the truth value estimation stage, an expectation maximization algorithm and a kernel Kalman filtering technology are applied, parameters are optimized, and the most probable truth value is estimated. According to the method, the kernel function and the Kalman filtering algorithm are used, the problem of time sequence data truth value discovery is solved, the truth value discovery effect is optimized, and the method has wide application value and use prospects in the field of big data.

The invention discloses an electric vehicle charging demand characteristic probability quantification method based on multivariate Gaussian mixture distribution, and relates to the field of vehicles, and the method comprises the steps: obtaining platform original data; preprocessing the original data of the platform to obtain charging demand characteristics of the electric vehicle; based on the initial model parameters and the charging demand characteristics of the electric vehicle, optimizing the initial model parameters by using an expectation maximization algorithm to obtain optimized model parameters; the initial model parameters are obtained through a random generation method; the model parameters comprise a weight, a mean value, a variance and a skewness coefficient; constructing a Gaussian mixture model based on the optimized model parameters; and quantifying the charging demand characteristics of the electric vehicle through a Gaussian mixture model. According to the invention, the accuracy and effectiveness of electric vehicle charging demand feature quantification are improved.

The invention discloses a hybrid antenna array direction of arrival estimation method based on noise marginalization SBL. The method comprises the following steps: initializing system parameters and a sampling grid set; establishing a hybrid antenna array receiving signal model, and initializing a hybrid beam forming matrix into a block diagonal matrix meeting constant modulus constraint; constructing a hierarchical probability model under a Bayesian framework, and introducing a noise precision parameter into signal prior; integral operation is carried out on the noise precision parameter, so that the signal posterior distribution is converted into student t distribution; an objective function is constructed, an expectation maximization algorithm is adopted to iteratively update a signal energy spectrum, and the process does not involve noise parameter estimation; an alternating iteration strategy is adopted, the signal energy is fixedly updated through inner circulation, and optimization is performed through a gradient descent method after outer circulation is fixed; after convergence, constructing a target function of off-grid estimation by reconstructing a covariance matrix; and searching the off-grid direction of the maximized objective function near a spectrum peak to obtain a final DOA estimation value.

The invention discloses a frequency domain adaptive clutter suppression method and system for a dual-polarization weather radar, and relates to the technical field of weather radar processing. According to the invention, pulse-level quality control and ground feature identification marking are carried out on radar original echoes; calculating power spectrums of the marking units, performing dynamic noise measurement and data quality grading, and screening out effective processing units; constructing a Gaussian mixture model of a power spectrum of an effective unit, and iteratively estimating model parameters through an expectation maximization algorithm to realize complete separation of a ground feature spectrum and a meteorological spectrum in a frequency domain; inverting single-channel parameters based on the separated meteorological spectrum; a cooperative spectrum separation strategy is adopted for horizontal and vertical polarization channels, and dual-polarization parameters such as differential reflectivity, correlation coefficients and differential phases are inverted based on the filtered dual-channel signals; according to the invention, high-precision separation of clutters and meteorological echoes is realized, the loss of meteorological signals is significantly reduced while ground features are effectively suppressed, and the accuracy and reliability of dual-polarization parameter inversion are improved.

The invention relates to the technical field of target tracking, and discloses an LSTM-KF hybrid tracking method based on parameter adaptation, which comprises the following steps: acquiring target position measurement data of a moving target, and extracting dual-channel characteristics of the target position measurement data through a sliding window; inputting the dual-channel features into a pre-trained dual-channel LSTM network to obtain a target position prediction result and a target speed prediction result; analyzing target motion characteristics based on a prediction result, selecting a corresponding dynamic model according to a motion mode, and performing target state estimation on the dynamic model through a Kalman filter; and finally, optimizing noise parameters of the Kalman filter by adopting an expectation maximization algorithm, carrying out weighted fusion on a prediction result of the dual-channel LSTM network and a target state estimation result, and outputting a final target state. According to the invention, high-precision and high-robustness target tracking in a complex environment is realized.

The invention discloses an intelligent flood forecasting method for coupling error correction and joint modeling, and relates to a deep learning and uncertainty modeling technology. At the input end, constructing a future random rainfall scene through hourly dynamic normal disturbance; the method comprises the following steps: at a model end, introducing a multi-structure and multi-objective function combination based on Kolmogorov-Arnold Networks and Transform, and forming a multi-member ensemble forecast; at an error end, a probabilistic modeling method based on a numerable asymmetric Laplacian mixed density network is provided, and fine error correction is realized; a Vine copula function is adopted to construct high-dimensional joint distribution, and a Bayesian model averaging and expectation maximization algorithm is combined to realize weighted fusion of multi-member posterior results; according to the method, uncertainty in flood forecasting can be comprehensively described, the stability and adaptability of a forecasting system are improved, and the method is suitable for a basin-level real-time flood ensemble forecasting scene.